Azure OpenAI Service
An Azure service that provides access to OpenAI’s GPT-3 models with enterprise capabilities.
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How to fix INFO:openai:error_code=404 error_message='Resource not found' error_param=None error_type=None message='OpenAI API error received' stream_error=False
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Anonymous
import os
import logging
import openai
from gpt_index import SimpleDirectoryReader, GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext
from langchain.llms import AzureOpenAI
from flask_cors import CORS
from flask_swagger_ui import get_swaggerui_blueprint
from flask import Flask, request, jsonify
app = Flask(__name__)
CORS(app)
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_KEY"] = 'Azure-api-key'
os.environ["OPENAI_API_BASE"] = 'https://chat-gpt-dev.openai.azure.com/'
os.environ["OPENAI_API_VERSION"]="2023-03-15-preview"
def load():
max_input_size = 5000
num_outputs = 4096
max_chunk_overlap = 20
chunk_size_limit = 5000
prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
llm_predictor = LLMPredictor(llm=AzureOpenAI(temperature=10, model_name="gpt35", max_tokens=num_outputs,))
documents = SimpleDirectoryReader(r"data").load_data()
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
index.save_to_disk('index.json')
return jsonify({'status': 'success', 'response': 'Data loaded successfully'})
load()
@app.route("/chatgpt", methods=['GET'])
def chatbot():
index = GPTSimpleVectorIndex.load_from_disk('index.json')
question = request.args.get('question')
if question:
answer = index.query(question, response_mode="compact")
return jsonify({'status': 'success', 'response': str(answer)})
else:
return "No question provided"
if __name__ == "__main__":
app.run(host='0.0.0.0', port=8000, threaded=True, debug=True)
Getting error as below: USING API_BASE: https://chat-gpt-dev.openai.azure.com/ INFO:openai:error_code=404 error_message='Resource not found' error_param=None error_type=None message='OpenAI API error received' stream_error=False INFO:openai:error_code=404 error_message='Resource not found' error_param=None error_type=None message='OpenAI API error received' stream_error=False INFO:openai:error_code=404 error_message='Resource not found' error_param=None error_type=None message='OpenAI API error received' stream_error=False INFO:openai:error_code=404 error_message='Resource not found' error_param=None error_type=None message='OpenAI API error received' stream_error=False INFO:openai:error_code=404 error_message='Resource not found' error_param=None error_type=None message='OpenAI API error received' stream_error=False INFO:openai:error_code=404 error_message='Resource not found' error_param=None error_type=None message='OpenAI API error received' stream_error=False Traceback (most recent call last): File "D:\azure_openAi.venv\Lib\site-packages\tenacity_init_.py", line 382, in call result = fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "D:\azure_openAi.venv\Lib\site-packages\gpt_index\embeddings\openai.py", line 147, in get_embeddings
data = openai.Embedding.create(input=list_of_text, engine=engine).data
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\azure_openAi.venv\Lib\site-packages\openai\api_resources\embedding.py", line 33, in create
response = super().create(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\azure_openAi.venv\Lib\site-packages\openai\api_resources\abstract\engine_api_resource.py", line 153, in create
response, _, api_key = requestor.request(
^^^^^^^^^^^^^^^^^^
File "D:\azure_openAi.venv\Lib\site-packages\openai\api_requestor.py", line 226, in request
resp, got_stream = self._interpret_response(result, stream)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\azure_openAi.venv\Lib\site-packages\openai\api_requestor.py", line 619, in _interpret_response
self._interpret_response_line(
File "D:\azure_openAi.venv\Lib\site-packages\openai\api_requestor.py", line 679, in _interpret_response_line
raise self.handle_error_response(
openai.error.InvalidRequestError: Resource not found
The above exception was the direct cause of the following exception:
Traceback (most recent call last): File "d:\azure_openAi\app.py", line 49, in <module>
load()
File "d:\azure_openAi\app.py", line 45, in load
index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\azure_openAi.venv\Lib\site-packages\gpt_index\indices\base.py", line 105, in from_documents
return cls(
^^^^
File "D:\azure_openAi.venv\Lib\site-packages\gpt_index\indices\vector_store\vector_indices.py", line 69, in init
super().__init__(
File "D:\azure_openAi.venv\Lib\site-packages\gpt_index\indices\vector_store\base.py", line 54, in init
super().__init__(
File "D:\azure_openAi.venv\Lib\site-packages\gpt_index\indices\base.py", line 74, in init
index_struct = self.build_index_from_nodes(nodes)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\azure_openAi.venv\Lib\site-packages\gpt_index\token_counter\token_counter.py", line 78, in wrapped_llm_predict
f_return_val = f(_self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\azure_openAi.venv\Lib\site-packages\gpt_index\indices\vector_store\base.py", line 217, in build_index_from_nodes
return self._build_index_from_nodes(nodes)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\azure_openAi.venv\Lib\site-packages\gpt_index\indices\vector_store\base.py", line 206, in _build_index_from_nodes
self._add_nodes_to_index(index_struct, nodes)
File "D:\azure_openAi.venv\Lib\site-packages\gpt_index\indices\vector_store\base.py", line 178, in _add_nodes_to_index
embedding_results = self._get_node_embedding_results(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\azure_openAi.venv\Lib\site-packages\gpt_index\indices\vector_store\base.py", line 96, in _get_node_embedding_results
) = self._service_context.embed_model.get_queued_text_embeddings()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\azure_openAi.venv\Lib\site-packages\gpt_index\embeddings\base.py", line 155, in get_queued_text_embeddings
embeddings = self._get_text_embeddings(cur_batch_texts)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\azure_openAi.venv\Lib\site-packages\gpt_index\embeddings\openai.py", line 261, in _get_text_embeddings
embeddings = get_embeddings(texts, engine=engine)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\azure_openAi.venv\Lib\site-packages\tenacity_init_.py", line 289, in wrapped_f
return self(f, *args, **kw)
^^^^^^^^^^^^^^^^^^^^
File "D:\azure_openAi.venv\Lib\site-packages\tenacity_init_.py", line 379, in call do = self.iter(retry_state=retry_state)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\azure_openAi.venv\Lib\site-packages\tenacity_init_.py", line 326, in iter
raise retry_exc from fut.exception()
tenacity.RetryError: RetryError[<Future at 0x248b31bf190 state=finished raised InvalidRequestError>]
{count} votes
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romungi-MSFT 43,656 Reputation points Microsoft Employee2023-05-19T11:14:23.7966667+00:00 @Anonymous Is it possible that your environment variables are not available in your application script in the virtual environment? I see the endpoint in the message but is the key available when the call is made. The error seems to indicate that is unable to authenticate. Do you need to add them directly in your app to check if it works as expected?
openai.api_type = "azure"openai.api_base = "AZURE_OPENAI_ENDPOINT"openai.api_version = "2023-05-15"openai.api_key = "AZURE_OPENAI_KEY"Try a simple API call to check if the endpoint is available and if the response is seen.
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Anonymous
2023-05-19T12:08:43.3966667+00:00 But getting error for this
Getting out put for this
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romungi-MSFT 43,656 Reputation points Microsoft Employee
2023-05-19T14:11:09.0966667+00:00 @Anonymous This confirms that the API endpoint works. I think in the first case where you are using the virtual environment the message states that the request was invalid. Do you see same message when you use the same endpoint as the second one?
I think in the first case the request to endpoint is somehow having some incorrect parameters or headers. You may try to print the request before the call and check what could be causing this.
Also, is there a document that you are following to run your first setup?
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Anonymous
2023-05-19T15:34:18.4766667+00:00
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